Selection of Primary and Recovery Supply and Demand Portfolios and Scheduling

Tadeusz Sawik

Chapter Chapter 10 in Supply Chain Disruption Management, 2020, pp 277-320 from Springer

Abstract: Abstract A multi-portfolio approach and stochastic MIP formulations with an embedded network flow problem are developed for selection of primary and recovery suppliers and assembly plants in the presence of supply chain disruption risks. Unlike most of reported research on supply chain disruption management a disruptive event is assumed to impact both a primary supplier of parts and a primary assembly plant of the finished products manufacturer. Then the manufacturer may choose alternate (recovery) suppliers and move production to alternate (recovery) plants along with transshipment of parts from the impacted primary plant to the recovery plants. The resulting allocation of unfulfilled demand for parts among recovery suppliers, the inventory of parts and unfulfilled demand for products among recovery assembly plants determines recovery supply, transshipment and demand portfolio, respectively. The selection of supply, transshipment and demand portfolios is determined simultaneously with production scheduling in assembly plants. Production scheduling An integrated decision-making Decision-making integrated approach with the perfect information about the potential future disruption scenarios is compared with a hierarchical approach with no such information available ahead of time. The best-case and worst-case analysis indicates that for the hierarchical approach the best-case and worst-case disruption scenarios are, respectively, subsets and supersets of the corresponding scenarios for the integrated approach. The findings also indicate that when all primary suppliers are completely shut down, a single sourcing recovery supply portfolio is usually selected, while multiple recovery plants may be selected, even if all primary plants are shut down. The proposed portfolio approach along with the stochastic MIP models that account for both time and cost of recovery may prove to be a flexible and efficient tool for supply chain disruption management. The major managerial insights are summarized at the end of this chapter.

Date: 2020
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DOI: 10.1007/978-3-030-44814-1_10

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